WHEN YOU JOIN VERIZON

You want more out of a career. A place to share your ideas freely — even if they’re daring or different. Where the true you can learn, grow, and thrive. At Verizon, we power and empower how people live, work and play by connecting them to what brings them joy. We do what we love — driving innovation, creativity, and impact in the world. Our V Team is a community of people who anticipate, lead, and believe that listening is where learning begins. In crisis and in celebration, we come together — lifting our communities and building trust in how we show up, everywhere & always. Want in? Join the #VTeamLife.

Join Verizon as we continue to grow our industry-leading network to improve the ways people, businesses, and things connect. We are looking for an experienced, talented and motivated Principal AI/ML Engineer to lead AI Industrialization for Verizon.
As a lead, you will provide technology leadership and drive technology discussions along with Enterprise Architecture, Data Science and Data Engineering teams.
You will also serve as a subject matter expert regarding the latest industry knowledge to improve the organization’s systems and/or processes related to Machine Learning, Deep Learning, Responsible AI, Gen AI, Natural Language Processing, Computer Vision and other AI practices.

You will lead the charter to Industrialize AI/ML model development, feature engineering, Model validation, deployment and Model Observability in both Real Time and Batch setup.- Designing, developing, and deploying end-to-end AI/ML solutions, including data pipelines, model training, deployment, monitoring, and optimization

• Deploying machine learning models - On Prem, Cloud and Kubernetes environments
• Creating and implementing data and ML pipelines for model inference, both in real-time and in batches.
• Architecting, designing, and implementing large-scale AI/ML systems in a production environment.
• Leading the consolidation and implementation of new concepts and processes in areas including information retrieval, distributed computing, large-scale system design, networking, data storage, security, artificial intelligence, natural language processing, UI design, and mobile.
• Setting the strategy for ML/AI tools and processes, determining the future needs of the business, and enhancing existing ML libraries and frameworks.
• Analyzing extensive and complex data sets to determine the most efficient methods for processing large volumes of data using Spark, Hive, and SQL.
• Monitor the performance of data pipelines and make improvements as necessary